During the past 10 years, this PPG has focused on documenting persistent inefficiency in U.S. healthcare. Yet we still have an incomplete understanding of the sources of Inefficiency. Nor are our methods and measures adequate to study how policy changes arising in the public or private sector will affects costs and quality. Finally, we are concerned about potential unintended consequences of policy changes adversely affecting vulnerable patients. This PPG application addresses these gaps with 5 distinct subprojects: Healthcare Efficiency among High Need, High Cost Patients. We use Medicare, Medicaid, and nursing home data to study the cost and quality of care for high-need and often high-cost patients within physician-hospital networks. We will study how these high-need patients are affected as healthcare systems evolve - will systems improve the continuity of care for high-need costly patients, or instead seek to shed them? Efficiency of Prescription Drug Use in the Medicare Population. Physician-hospital networks are used to measure differences across patients in their use of efficient and inefficient pharmaceutical use, and the implications for outcomes and downstream costs. We focus on patient cohorts with hip fractures and AMI, as well as under-age-65 disabled Medicare enrollees, African-Americans, and the oldest-old. Understanding and Improving Episode-based Hospital Care. Using a unique dataset on surgery in Michigan hospitals, we seek to understand variations in surgical safety/quality and in healthcare expenditures during the Initial admission and post-discharge. A key question is how Medicare bundled payment reform will affect quality for the over-65, and whether there are spillover effects to the under-65. Measurement and Determinants of Healthcare Efficiency. We estimate models of provider efficiency in static models (with Medicare, Medicaid, and private insurance data when available), between the U.S. and Canada, and with dynamic measures (looking at changes overtime in AMI treatments). Advancing Measures for Risk Adjustment and Performance Assessment. To improve risk adjustment, we develop new health measures from the Health and Retirement Study (HRS) and Medicare. We also pilot patient-reported and biomarker-based measures of health in three sites across the U.S. (Dartmouth, Pennsylvania, and Kaiser Colorado), and further study how feedback to clinicians affects care quality. All 5 subprojects are supported by an Administrative Core, a Data Core, and a Measurement Core.